Hi, I'm Alexander Kozlovsky, one of Pony ORM authors.
I'll try to answer some questions
> why such an obsession with "pure" Python?
With this statement we are trying to say Pony is not just to offer some syntax sugar. The end goal of Pony ORM is to understand the semantic of each Python generator which can be translated to database query and then do translation accordingly.
The direct benefit of this is that generator query are more high-level then resulted SQL, and can be much easier to understand and refactor. The simplest example is an automatic converting of attribute path traversing such as grade.student.group.department.name to a minimal set of SQL joins. Pony also can use attribute path in reverse direction, from “one” to “many”, such as department.groups.students.gpa, which in this case resulted in a multiset of all ‘gpa’ values for this department. Also you can see examples of query optimization in the documentation, for example when the subquery is converted to LEFT JOIN with GROUP BY, where it can give performance benefit.
There are more distant benefits of the semantic translation. In future, we plan to add support of denormalized database schemas. Such denormalization will take place on a physical level (i.e. in the database) while on the logical level (in Python) all queries and logic will remain the same. But first of all we are going to add the migration support.
Another future plan is to add support of MongoDB. This is a distant plan, but I think Pony architecture will allow this. After that, migration of PonyORM-based project from relational databases to noSQL and vice versa will be possible.
> An ORM like Django's offers a higher level of abstraction
I disagree with this. I think, any Django query can be written in PonyORM much more concisely, and there are easy-to-understand PonyORM queries which cannot be expressed in Django easily.
> why such an obsession with "pure" Python?
With this statement we are trying to say Pony is not just to offer some syntax sugar. The end goal of Pony ORM is to understand the semantic of each Python generator which can be translated to database query and then do translation accordingly.
The direct benefit of this is that generator query are more high-level then resulted SQL, and can be much easier to understand and refactor. The simplest example is an automatic converting of attribute path traversing such as grade.student.group.department.name to a minimal set of SQL joins. Pony also can use attribute path in reverse direction, from “one” to “many”, such as department.groups.students.gpa, which in this case resulted in a multiset of all ‘gpa’ values for this department. Also you can see examples of query optimization in the documentation, for example when the subquery is converted to LEFT JOIN with GROUP BY, where it can give performance benefit.
There are more distant benefits of the semantic translation. In future, we plan to add support of denormalized database schemas. Such denormalization will take place on a physical level (i.e. in the database) while on the logical level (in Python) all queries and logic will remain the same. But first of all we are going to add the migration support.
Another future plan is to add support of MongoDB. This is a distant plan, but I think Pony architecture will allow this. After that, migration of PonyORM-based project from relational databases to noSQL and vice versa will be possible.
> An ORM like Django's offers a higher level of abstraction
I disagree with this. I think, any Django query can be written in PonyORM much more concisely, and there are easy-to-understand PonyORM queries which cannot be expressed in Django easily.
Also, Pony ORM completely eliminates N+1 select problem.